"""simple docstring"""


from collections.abc import Generator
from math import sin





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bytes:




						'''simple docstring'''



						if len(__lowerCAmelCase							) != 32:
												raise ValueError("Input must be of length 32"							)

						lowerCamelCase__   =B""
						for i in [3, 2, 1, 0]:
												little_endian += string_aa[8 * i : 8 * i + 8]
						return little_endian





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bytes:




						'''simple docstring'''



						if i < 0:
												raise ValueError("Input must be non-negative"							)

						lowerCamelCase__   =format(__lowerCAmelCase  ,       "08x"							)[-8:]
						lowerCamelCase__   =B""
						for i in [3, 2, 1, 0]:
												little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"							)
						return little_endian_hex





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bytes:




						'''simple docstring'''



						lowerCamelCase__   =B""
						for char in message:
												bit_string += format(__lowerCAmelCase  ,       "08b"							).encode("utf-8"							)
						lowerCamelCase__   =format(len(__lowerCAmelCase							)  ,       "064b"							).encode("utf-8"							)

						# Pad bit_string to a multiple of 512 chars
						bit_string += b"1"
						while len(__lowerCAmelCase							) % 512 != 448:
												bit_string += b"0"
						bit_string += to_little_endian(start_len[32:]							) + to_little_endian(start_len[:32]							)

						return bit_string





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Generator[list[int], None, None]:




						'''simple docstring'''



						if len(__lowerCAmelCase							) % 512 != 0:
												raise ValueError("Input must have length that's a multiple of 512"							)

						for pos in range(0  ,       len(__lowerCAmelCase							)  ,       512							):
												lowerCamelCase__   =bit_string[pos : pos + 512]
												lowerCamelCase__   =[]
												for i in range(0  ,       512  ,       32							):
																		block_words.append(int(to_little_endian(block[i : i + 32]							)  ,       2							)							)
												yield block_words





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if i < 0:
												raise ValueError("Input must be non-negative"							)

						lowerCamelCase__   =format(__lowerCAmelCase  ,       "032b"							)
						lowerCamelCase__   =""
						for c in i_str:
												new_str += "1" if c == "0" else "0"
						return int(__lowerCAmelCase  ,       2							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						return (a + b) % 2**32





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if i < 0:
												raise ValueError("Input must be non-negative"							)
						if shift < 0:
												raise ValueError("Shift must be non-negative"							)
						return ((i << shift) ^ (i >> (32 - shift))) % 2**32





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bytes:




						'''simple docstring'''



						lowerCamelCase__   =preprocess(__lowerCAmelCase							)

						lowerCamelCase__   =[int(2**32 * abs(sin(i + 1							)							)							) for i in range(64							)]

						# Starting states
						lowerCamelCase__   =0x67_45_23_01
						lowerCamelCase__   =0xEF_CD_AB_89
						lowerCamelCase__   =0x98_BA_DC_FE
						lowerCamelCase__   =0x10_32_54_76

						lowerCamelCase__   =[
						    7,
						    12,
						    17,
						    22,
						    7,
						    12,
						    17,
						    22,
						    7,
						    12,
						    17,
						    22,
						    7,
						    12,
						    17,
						    22,
						    5,
						    9,
						    14,
						    20,
						    5,
						    9,
						    14,
						    20,
						    5,
						    9,
						    14,
						    20,
						    5,
						    9,
						    14,
						    20,
						    4,
						    11,
						    16,
						    23,
						    4,
						    11,
						    16,
						    23,
						    4,
						    11,
						    16,
						    23,
						    4,
						    11,
						    16,
						    23,
						    6,
						    10,
						    15,
						    21,
						    6,
						    10,
						    15,
						    21,
						    6,
						    10,
						    15,
						    21,
						    6,
						    10,
						    15,
						    21,
						]

						# Process bit string in chunks, each with 16 32-char words
						for block_words in get_block_words(__lowerCAmelCase							):
												lowerCamelCase__   =aa
												lowerCamelCase__   =ba
												lowerCamelCase__   =ca
												lowerCamelCase__   =da

												# Hash current chunk
												for i in range(64							):
																		if i <= 15:
																								# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																								lowerCamelCase__   =d ^ (b & (c ^ d))
																								lowerCamelCase__   =i
																		elif i <= 31:
																								# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																								lowerCamelCase__   =c ^ (d & (b ^ c))
																								lowerCamelCase__   =(5 * i + 1) % 16
																		elif i <= 47:
																								lowerCamelCase__   =b ^ c ^ d
																								lowerCamelCase__   =(3 * i + 5) % 16
																		else:
																								lowerCamelCase__   =c ^ (b | not_aa(__lowerCAmelCase							))
																								lowerCamelCase__   =(7 * i) % 16
																		lowerCamelCase__   =(f + a + added_consts[i] + block_words[g]) % 2**32
																		lowerCamelCase__   =d
																		lowerCamelCase__   =c
																		lowerCamelCase__   =b
																		lowerCamelCase__   =sum_aa(__lowerCAmelCase  ,       left_rotate_aa(__lowerCAmelCase  ,       shift_amounts[i]							)							)

												# Add hashed chunk to running total
												lowerCamelCase__   =sum_aa(__lowerCAmelCase  ,       __lowerCAmelCase							)
												lowerCamelCase__   =sum_aa(__lowerCAmelCase  ,       __lowerCAmelCase							)
												lowerCamelCase__   =sum_aa(__lowerCAmelCase  ,       __lowerCAmelCase							)
												lowerCamelCase__   =sum_aa(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   =reformat_hex(__lowerCAmelCase							) + reformat_hex(__lowerCAmelCase							) + reformat_hex(__lowerCAmelCase							) + reformat_hex(__lowerCAmelCase							)
						return digest


if __name__ == "__main__":
	import doctest

	doctest.testmod()



"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  List[Any]    				=			'''Wav2Vec2FeatureExtractor'''
				A__ :  Any    				=			'''AutoTokenizer'''

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										super().__init__(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False

				@classmethod
				def 						_a      ( cls      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										try:
																return super().from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
										except OSError:
																warnings.warn(
																    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
																    " include a `tokenizer_class` attribute is deprecated and will be "
																    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
																    " attribute to either your `config.json` or `tokenizer_config.json` "
																    "file to suppress this warning: "      ,					_lowerCamelCase      ,					)

																lowerCamelCase__   =WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )
																lowerCamelCase__   =WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase      ,					**_lowerCamelCase )

																return cls(feature_extractor=_lowerCamelCase      ,					tokenizer=_lowerCamelCase )

				def __call__( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*_lowerCamelCase      ,					**_lowerCamelCase )

										if "raw_speech" in kwargs:
																warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
																lowerCamelCase__   =kwargs.pop("raw_speech" )
										else:
																lowerCamelCase__   =kwargs.pop("audio"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("sampling_rate"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("text"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if audio is None and text is None:
																raise ValueError("You need to specify either an `audio` or `text` input to process." )

										if audio is not None:
																lowerCamelCase__   =self.feature_extractor(_lowerCamelCase      ,					*_lowerCamelCase      ,					sampling_rate=_lowerCamelCase      ,					**_lowerCamelCase )
										if text is not None:
																lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					**_lowerCamelCase )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																lowerCamelCase__   =encodings["input_ids"]
																return inputs

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor.pad(*_lowerCamelCase      ,					**_lowerCamelCase )

										lowerCamelCase__   =kwargs.pop("input_features"      ,					_lowerCamelCase )
										lowerCamelCase__   =kwargs.pop("labels"      ,					_lowerCamelCase )
										if len(_lowerCamelCase ) > 0:
																lowerCamelCase__   =args[0]
																lowerCamelCase__   =args[1:]

										if input_features is not None:
																lowerCamelCase__   =self.feature_extractor.pad(_lowerCamelCase      ,					*_lowerCamelCase      ,					**_lowerCamelCase )
										if labels is not None:
																lowerCamelCase__   =self.tokenizer.pad(_lowerCamelCase      ,					**_lowerCamelCase )

										if labels is None:
																return input_features
										elif input_features is None:
																return labels
										else:
																lowerCamelCase__   =labels["input_ids"]
																return input_features

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,					**_lowerCamelCase )

				def 						_a      ( self      ,					*_lowerCamelCase      ,					**_lowerCamelCase ):
										return self.tokenizer.decode(*_lowerCamelCase      ,					**_lowerCamelCase )






				@contextmanager
				def 						_a      ( self ):
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call." )
										lowerCamelCase__   =True
										lowerCamelCase__   =self.tokenizer
										yield
										lowerCamelCase__   =self.feature_extractor
										lowerCamelCase__   =False


